SoroushVahidi/scidocs huggingface dataset:
Applying connectionist modal logics to distributed knowledge representation problems
(2004)

Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.
Epigenetics and Signaling Pathways in Glaucoma
(2017)

Glaucoma is the most common cause of irreversible blindness worldwide. This neurodegenerative disease becomes more prevalent with aging, but predisposing genetic and environmental factors also contribute to increased risk. Emerging evidence now suggests that epigenetics may also be involved, which provides potential new therapeutic targets. These three factors work through several pathways, including TGF-β, MAP kinase, Rho kinase, BDNF, JNK, PI-3/Akt, PTEN, Bcl-2, Caspase, and Calcium-Calpain signaling. Together, these pathways result in the upregulation of proapoptotic gene expression, the downregulation of neuroprotective and prosurvival factors, and the generation of fibrosis at the trabecular meshwork, which may block aqueous humor drainage. Novel therapeutic agents targeting these pathway members have shown preliminary success in animal models and even human trials, demonstrating that they may eventually be used to preserve retinal neurons and vision.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
(2014)

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1, 000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.
A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods.
(2007)

In this initial pilot study, a controlled clinical comparison was made of attention perforance in children with attention deficit-hyperactivity disorder (ADHD) in a virtual reality (VR) classroom. Ten boys diagnosed with ADHD and ten normal control boys participated in the study. Groups did not significantly differ in mean age, grade level, ethnicity, or handedness. No participants reported simulator sickness following VR exposure. Children with ADHD exhibited more omission errors, commission errors, and overall body movement than normal control children in the VR classroom. Children with ADHD were more impacted by distraction in the VR classroom. VR classroom measures were correlated with traditional ADHD assessment tools and the flatscreen CPT. Of note, the small sample size incorporated in each group and higher WISC-III scores of normal controls might have some bearing on the overall interpretation of results. These data suggested that the Virtual Classroom had good potential for controlled performance assessment within an ecologically valid environment and appeared to parse out significant effects due to the presence of distraction stimuli.
Does Twitter language reliably predict heart disease? A commentary on Eichstaedt et al. (2015a)
(2018)

We comment on Eichstaedt et al.'s (2015a) claim to have shown that language patterns among Twitter users, aggregated at the level of US counties, predicted county-level mortality rates from atherosclerotic heart disease (AHD), with "negative" language being associated with higher rates of death from AHD and "positive" language associated with lower rates. First, we examine some of Eichstaedt et al.'s apparent assumptions about the nature of AHD, as well as some issues related to the secondary analysis of online data and to considering counties as communities. Next, using the data files supplied by Eichstaedt et al., we reproduce their regression- and correlation-based models, substituting mortality from an alternative cause of death-namely, suicide-as the outcome variable, and observe that the purported associations between "negative" and "positive" language and mortality are reversed when suicide is used as the outcome variable. We identify numerous other conceptual and methodological limitations that call into question the robustness and generalizability of Eichstaedt et al.'s claims, even when these are based on the results of their ridge regression/machine learning model. We conclude that there is no good evidence that analyzing Twitter data in bulk in this way can add anything useful to our ability to understand geographical variation in AHD mortality rates.
Cascading Bandits for Large-Scale Recommendation Problems
(2016)

Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend K most attractive items from a large set of L candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items L. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
(2009)

TheSGD-QN algorithm is a stochastic gradient descent algorithm that m akes careful use of secondorder information and splits the parameter update into inde pendently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient escent but requires less iterations to achieve the same accuracy. This algorith m won the “Wild Track” of the first PASCAL Large Scale Learning Challenge (Sonnenburg et al., 2008 ).
Autonomous human-robot proxemics: socially aware navigation based on interaction potential
(2017)

To enable situated human–robot interaction (HRI), an autonomous robot must both understand and control proxemics—the social use of space—to employ natural communication mechanisms analogous to those used by humans. This work presents a computational framework of proxemics based on data-driven probabilistic models of how social signals (speech and gesture) are produced (by a human) and perceived (by a robot). The framework and modelswere implemented as autonomous proxemic behavior systems for sociable robots, including: (1) a sampling-based method for robot proxemic goal state estimation with respect to human–robot distance and orientation parameters, (2) a reactive proxemic controller for goal state realization, and (3) a cost-based trajectory planner for maximizing automated robot speech and gesture recognition rates along a path to the goal state. Evaluation results indicate that the goal state estimation and realization significantly improve upon past work in human–robot proxemics with respect to “interaction potential”—predicted automated speech and gesture recognition rates as the robot enters into and engages in faceto-face social encounters with a human user—illustrating their efficacy to support richer robot perception and autonomy in HRI. Electronic supplementary material The online version of this article (doi:10.1007/s10514-016-9572-2) contains supplementary material, which is available to authorized users. B Ross Mead rossmead@usc.edu 1 3710 McClintock Avenue, RTH 423, Los Angeles, CA 90089-0781, USA 2 3710 McClintock Avenue, RTH 407, Los Angeles, CA 90089-0781, USA
Advanced Maintenance Simulation by Means of Hand-Based Haptic Interfaces
(2009)

Aerospace industry has been involved in virtual simulation for design and testing since the birth of virtual reality. Today this industry is showing a growing interest in the development of haptic-based maintenance training applications, which represent the most advanced way to simulate maintenance and repair tasks within a virtual environment by means of a visual-haptic approach. The goal is to allow the trainee to experiment the service procedures not only as a workflow reproduced at a visual level but also in terms of the kinaesthetic feedback involved with the manipulation of tools and components. This study, conducted in collaboration with aerospace industry specialists, is aimed to the development of an immersive virtual capable of immerging the trainees into a virtual environment where mechanics and technicians can perform maintenance simulation or training tasks by directly manipulating 3D virtual models of aircraft parts while perceiving force feedback through the haptic interface. The proposed system is based on ViRstperson, a virtual reality engine under development at the Italian Center for Aerospace Research (CIRA) to support engineering and technical activities such as design-time maintenance procedure validation, and maintenance training. This engine has been extended to support haptic-based interaction, enabling a more complete level of interaction, also in terms of impedance control, and thus fostering the development of haptic knowledge in the user. The user’s “sense of touch” within the immersive virtual environment is simulated through an Immersion CyberForce® hand-based force-feedback device. Preliminary testing of the proposed system seems encouraging.
Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping
(2012)

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.
E-TOURISM: AN INNOVATIVE APPROACH FOR THE SMALL AND MEDIUM-SIZED TOURISM ENTERPRISES (SMTES) IN KOREA
(2005)

This paper deals with e-tourism, innovation and growth. The Internet is revolutionising the distribution of tourism information and sales. The Korean small and medium-sized tourism enterprises (SMTEs) with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This paper examines problems and solutions related to electronic commerce in the tourism industry and suggests recommendations for successful e-commerce strategies in tourism to be applied by the industry and the government in Korea. Introduction The definitions of tourism innovation (e.g. product, service and technological innovations) remains unclear, with the exception maybe of the Internet. New technologies can produce an essential contribution to tourism development. For tourism businesses, the Internet offers the potential to make information and booking facilities available to large numbers of tourists at relatively low costs. It also provides a tool for communication between tourism suppliers, intermediaries, as well as end-consumers. OECD (2000) revealed that the advent of Internet-based electronic commerce offers considerable opportunities for firms to expand their customer base, enter new product markets and rationalise their business. WTO (2001) also indicated that electronic business offers SMEs the opportunity to undertake their business in new and more cost-effective ways. According to WTO, the Internet is revolutionising the distribution of tourism information and sales. An increasing proportion of Internet users are buying on–line and tourism will gain a larger and larger share of the online commerce market. Obviously, the Internet is having a major impact as a source of information for tourism. However, the SMTEs are facing more stringent impediments to the adoption of new information technology, in particular, e-business. Part of the problem relates to the scale and affordability of information technology, as well as the facility of implementation within rapidly growing and changing organisations. In addition, new solutions configured for large, stable, and internationally-oriented firms do not fit well for small, dynamic, and locally-based tourism firms. Despite these challenges, SMTEs with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This implies equal access to telecom infrastructure, as well as to marketing management and education. According to a UN report (2001), “it is not the cost of being there, on the on-line market place, which must be reckoned with, but the cost of not being there.” It is certain that embracing digital communication and information technology is no longer an option, but a necessity. Thus, one of the most important characteristics of
The Effectiveness of Intrinsic and Extrinsic Motivations : A Study of Malaysian Amway Company ’ s Direct Sales Forces
(2013)

This research utilizes the survey questionnaire data collected in May/June 2010 from 200 Amway Company’s direct sales forces in Klang Valley areas (Malaysia) to analyze the effectiveness of intrinsic and extrinsic motivation in influencing job satisfaction. The research will be analyzed using the well-established correlation analysis, regression analysis, independent sample t-test, and one-way ANOVA. There are four major findings. First, there is a relationship between intrinsic and extrinsic motivations with job satisfaction. According to the correlation value, intrinsic motivation compared to extrinsic motivation tends to contribute more in job satisfaction. Second, there are significant and positive relationship between intrinsic and extrinsic motivations and job satisfaction. Both of the intrinsic and extrinsic motivation is identified as the predictor for job satisfaction. In other words, they are both significantly contribute in better job satisfaction. Third, there is no difference between gender and intrinsic and extrinsic motivations. Hence, gender is not a factor that affects both of the intrinsic and extrinsic motivations. Lastly, the result indicates that there is a difference between age and intrinsic and extrinsic motivations. Therefore, age is the factor that influences on both intrinsic and extrinsic motivations. Last but not least, the results have demonstrated the effectiveness of intrinsic and extrinsic motivations in influencing job satisfaction among the Amway Company’s direct sales forces, as well as establishing appropriate intrinsic motivations may promote higher job satisfaction.
A robust adaptive mixing control for improved forward flight of a tilt-rotor UAV
(2016)

This work presents the modeling and control of a tilt-rotor UAV, with tail controlled surfaces, for path tracking with improved forward flight. The dynamic model is obtained using the Euler-Lagrange formulation considering the aerodynamic forces and torques exerted on the horizontal and vertical stabilizers, and fuselage. For control design purposes, the equations of motion are linearized around different operation points to cover a large range of forward velocity. Based on these linearized dynamic models, a mixed H2/H∞ robust controller is designed for each operation point. Therefore, an adaptive mixing scheme is used to perform an on-line smooth gain-scheduling between them. Simulation results show the control strategy efficiency when the UAV is designated to have a forward acceleration and perform a circular trajectory subject to a wind disturbance.
Fatigue in soccer: a brief review.
(2005)

This review describes when fatigue may develop during soccer games and the potential physiological mechanisms that cause fatigue in soccer. According to time-motion analyses and performance measures during match-play, fatigue or reduced performance seems to occur at three different stages in the game: (1) after short-term intense periods in both halves; (2) in the initial phase of the second half; and (3) towards the end of the game. Temporary fatigue after periods of intense exercise in the game does not appear to be linked directly to muscle glycogen concentration, lactate accumulation, acidity or the breakdown of creatine phosphate. Instead, it may be related to disturbances in muscle ion homeostasis and an impaired excitation of the sarcolemma. Soccer players' ability to perform maximally is inhibited in the initial phase of the second half, which may be due to lower muscle temperatures compared with the end of the first half. Thus, when players perform low-intensity activities in the interval between the two halves, both muscle temperature and performance are preserved. Several studies have shown that fatigue sets in towards the end of a game, which may be caused by low glycogen concentrations in a considerable number of individual muscle fibres. In a hot and humid environment, dehydration and a reduced cerebral function may also contribute to the deterioration in performance. In conclusion, fatigue or impaired performance in soccer occurs during various phases in a game, and different physiological mechanisms appear to operate in different periods of a game.
Combining Gradient and Albedo Data for Rotation Invariant Classification of 3D Surface Texture
(2003)

We present a new texture classification scheme which is invariant to surface-rotation. Many texture classification approaches have been presented in the past that are image-rotation invariant, However, image rotation is not necessarily the same as surface rotation. We have therefore developed a classifier that uses invariants that are derived from surface properties rather than image properties. Previously we developed a scheme that used surface gradient (normal) fields estimated using photometric stereo. In this paper we augment these data with albedo information and an also employ an additional feature set: the radial spectrum. We used 30 real textures to test the new classifier. A classification accuracy of 91% was achieved when albedo and gradient 1D polar and radial features were combined. The best performance was also achieved by using 2D albedo and gradient spectra. The classification accuracy is 99%.
A Software-Based Sonar Ranging Sensor for Smart Phones
(2015)

We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.
Recent Advances in Augmented Reality
(2001)

The field of Augmented Reality (AR) has existed for just over one decade, but the growth and progress in the past few years has been remarkable. In 1997, the first author published a survey [3] (based on a 1995 SIGGRAPH course lecture) that defined the field, described many problems, and summarized the developments up to that point. Since then, the field has grown rapidly. In the late 1990s, several conferences specializing in this area were started, including the International Workshop and Symposium on Augmented Reality [29], the International Symposium on Mixed Reality [30], and the Designing Augmented Reality Environments workshop. Some wellfunded interdisciplinary consortia were formed that focused on AR, notably the Mixed Reality Systems Laboratory [50] in Japan and Project ARVIKA [61] in Germany. A freely-available software toolkit (the ARToolkit) for rapidly building AR applications is now available [2]. Because of this wealth of new developments, an updated survey is needed to guide and encourage further research in this exciting area.
Exploring the Benefits of Augmented Reality Documentation for Maintenance and Repair
(2011)

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.